{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['bird', 'car', 'dog', 'lizard', 'turtle'],\n",
       " ['bird_gt.txt', 'car_gt.txt', 'dog_gt.txt', 'lizard_gt.txt', 'turtle_gt.txt'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import torch as t\n",
    "\n",
    "root = os.getcwd()\n",
    "dir = 'tiny_vid'\n",
    "root = os.path.join(root,dir)\n",
    "data_dir = []\n",
    "label_dir = []\n",
    "for each in os.listdir(root):\n",
    "    if '.txt' in each:\n",
    "        label_dir.append(each)\n",
    "    elif '.md' not in each:\n",
    "        data_dir.append(each)\n",
    "data_dir,label_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_path = []\n",
    "bbox_path = []\n",
    "for each in data_dir:\n",
    "    for img in os.listdir(os.path.join(root,each))[:180]:\n",
    "        img_path.append(os.path.join(os.path.join(root,each),img))\n",
    "        \n",
    "for each in label_dir:\n",
    "    bbox_path.append((os.path.join(root,each)))\n",
    "len(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=128x128 at 0x1E25CE3CA00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.open(img_path[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=128x128 at 0x1E25CE22580>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs = [Image.open(i)for i in img_path]\n",
    "imgs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([900])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = np.zeros((180,))\n",
    "l2 = np.ones((180,))\n",
    "l3 = l2.copy()*2\n",
    "l4 = l2.copy()*3\n",
    "l5 = l2.copy()*4\n",
    "\n",
    "labels = np.concatenate((l1,l2,l3,l4,l5))\n",
    "labels = t.from_numpy(labels)\n",
    "labels.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 46,   0, 123,  70],\n",
       "       [  4,  17,  84,  83],\n",
       "       [ 14,  13, 105,  99],\n",
       "       ...,\n",
       "       [ 13,  38,  94,  82],\n",
       "       [ 13,   4, 100,  63],\n",
       "       [  2,   7, 127, 125]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for each in bbox_path:\n",
    "    if each == bbox_path[0]:\n",
    "        bbox_data = pd.read_csv(each,sep=' ',header=None,index_col=None)\n",
    "        bbox_data[0] -= 1\n",
    "        bbox_data = bbox_data.iloc[:180]\n",
    "        bbox_data = bbox_data.drop(0,axis=1)\n",
    "        bbox_data = bbox_data.values\n",
    "    else:\n",
    "        data = pd.read_csv(each,sep=' ',header=None,index_col=None)\n",
    "        data[0]-=1\n",
    "        data = data.iloc[:180]\n",
    "        data = data.drop(0,axis=1)\n",
    "        data = data.values\n",
    "        bbox_data = np.concatenate((bbox_data,data),axis=0)\n",
    "bbox_data\n",
    "# bbox_data = t.from_numpy(bbox_data)\n",
    "# bbox_data.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 70,  66,  86, 111,  92,  49,  35,  88, 113,  87, 121,  84,  98,\n",
       "        117, 111,  78,  80,  81, 121, 114,  93,  62,  57, 122,  31, 103,\n",
       "         89, 114,  64,  76,  90,  56,  80, 128,  34,  79,  62,  56,  77,\n",
       "        104,  89, 112,  46,  89, 127,  63,  89, 104,  70,  75,  46,  40,\n",
       "         35,  50,  63,  90,  30,  61, 128,  96,  32,  93,  47, 110,  56,\n",
       "         97, 119, 114,  99, 105,  57,  37,  99,  55,  99, 108, 114,  46,\n",
       "         30, 118,  44,  89, 106,  53, 123,  93, 101, 117,  66,  47,  67,\n",
       "         36, 101, 122,  54,  66,  35,  77,  53,  69, 126,  88, 124,  94,\n",
       "        127,  54, 121, 113,  83,  79,  45,  66,  91,  36,  89, 103,  53,\n",
       "         99, 127,  85,  34,  52,  71,  98, 126,  57, 105,  67,  36,  38,\n",
       "         97, 100,  38, 123,  80,  36, 106, 108,  69,  67,  90,  46,  35,\n",
       "        122,  82,  47, 124,  54,  67,  46,  90,  88,  55, 110,  50,  80,\n",
       "         71,  73,  65, 108,  75,  31, 113, 115, 106, 128,  32, 122,  73,\n",
       "         48, 122, 107, 124, 121,  65,  77, 119, 127,  48, 126,  84,  56,\n",
       "         49,  36,  40,  54,  51, 116,  88,  63,  70,  72,  42, 111,  42,\n",
       "         39,  86,  72,  54,  44,  60,  45,  32,  83,  80,  58,  34,  96,\n",
       "         39,  37,  31,  86,  39,  53,  50,  38, 111,  52,  79,  55,  56,\n",
       "         66,  49,  30,  31,  78,  68, 114,  44,  36,  39,  34,  62,  83,\n",
       "         43,  47,  49,  72,  41,  53,  59,  43,  30,  30,  36,  35,  76,\n",
       "         64,  48,  61,  39,  65, 109,  42,  41,  40,  96,  98,  47,  38,\n",
       "         89,  31,  48,  34,  68,  44,  35,  68,  50,  50,  35,  51, 103,\n",
       "         53,  53,  69,  45,  76,  55,  47,  33,  77,  33,  38,  52,  70,\n",
       "         63,  30,  31,  30,  75,  42,  58,  83,  56,  69,  63,  53,  55,\n",
       "         43,  53,  92, 100,  62,  46,  45,  72,  44,  44,  48,  36,  37,\n",
       "         67,  40,  59,  90,  82,  53,  63,  44,  49,  84,  44,  51,  65,\n",
       "         82,  55,  63,  36,  81,  85,  56,  52,  45,  41,  77,  54,  44,\n",
       "         50,  61,  47, 101,  79,  85,  32, 103,  73,  43,  31,  41,  31,\n",
       "         78, 107, 120,  51,  53,  35,  36,  57,  35,  53, 101,  93,  62,\n",
       "        128, 100, 101,  92, 123,  88,  55,  77,  61,  85, 105,  78,  34,\n",
       "        104,  78, 120,  64, 119, 111,  95, 106,  91, 105,  97, 110,  67,\n",
       "         56,  57,  64,  93, 120,  78, 113,  93, 122,  38,  79,  50,  94,\n",
       "         40, 100, 124,  71,  88,  93, 117, 103,  89,  94,  84, 117,  67,\n",
       "         64,  91,  53,  74,  76,  73,  60,  89,  34, 101, 111,  59,  37,\n",
       "         48,  70, 118,  37,  98,  85,  45,  45,  97, 106,  76, 108,  87,\n",
       "         67, 128, 125,  43, 102, 127,  77,  40,  95, 120,  78, 110, 128,\n",
       "        114,  98,  65, 124,  74, 122, 101,  50,  87,  45, 115,  93, 127,\n",
       "         94,  94,  54, 111, 110,  51,  67,  60,  47, 103,  97, 120,  70,\n",
       "         58,  51, 101,  79,  37,  58, 101,  99,  83,  85, 105,  89, 119,\n",
       "         96, 101,  88,  48, 112,  81,  49,  78, 119,  35, 118,  85,  85,\n",
       "         50, 104, 102,  84,  79, 122,  81,  55,  95, 109, 110,  96,  54,\n",
       "         67,  59, 101,  57,  59,  70,  74, 111,  41,  98, 127,  68, 122,\n",
       "         54, 100, 128,  75, 107,  57,  94,  65, 113,  95,  49,  98,  72,\n",
       "         99,  53,  84,  57, 103,  65, 115,  96,  81,  79,  63, 126,  97,\n",
       "         77,  89,  83,  79,  60,  86,  83,  77,  49,  96, 101,  70,  83,\n",
       "         94,  64,  76,  61,  44,  58,  71,  31,  92, 120,  83, 103,  70,\n",
       "         92,  67,  76,  49,  89,  91, 102,  98,  98,  95,  89,  94, 103,\n",
       "         32,  45,  94, 106,  79, 104, 102,  89,  56,  72,  64, 110,  36,\n",
       "         85,  85, 100,  74,  89,  52,  72,  81, 124, 103,  89,  76,  59,\n",
       "        101,  93, 107, 117,  86,  76,  83,  38, 108,  95, 102, 108,  96,\n",
       "         71,  78,  37, 127,  98, 101,  71,  79, 100, 106, 110,  70,  68,\n",
       "        113,  55,  89,  97,  87,  33,  77,  86, 101,  87,  82, 117,  49,\n",
       "         31,  76,  96,  85,  96,  66,  47,  34,  49,  85, 108,  82, 121,\n",
       "        116,  97,  69,  72,  78,  97, 120, 116,  95,  30,  91,  68,  81,\n",
       "         76,  44,  83,  63,  45, 113, 106,  69,  76,  43,  68,  65,  49,\n",
       "         92,  51,  97,  89,  97,  62,  99,  97,  87, 102,  37,  95,  87,\n",
       "         95,  73, 117,  92, 109,  35,  80,  71,  39, 123, 123,  57,  38,\n",
       "         39,  55,  57, 107,  47, 101,  45, 106,  98,  69,  54,  51,  71,\n",
       "         82,  64,  88,  41,  34,  73,  86,  92,  97,  30,  41,  84,  78,\n",
       "         98,  75,  42,  70,  86,  47,  45,  80,  84,  50,  60,  62, 117,\n",
       "        104, 103,  38,  68,  41,  82,  54,  85,  32,  31, 118,  71,  51,\n",
       "         66,  45, 124, 115,  51,  73,  57,  48, 107,  97,  35,  44, 106,\n",
       "        116,  34,  49,  70,  54,  39,  39,  51,  59,  62,  54,  39,  48,\n",
       "         81,  99, 123,  66,  37, 104,  91,  32,  34, 102,  56,  49,  54,\n",
       "        107,  42,  64,  74,  62,  39,  39,  46,  78,  58,  62,  30, 128,\n",
       "         49,  88, 109,  52,  41,  75,  67,  59,  87,  80,  62,  80,  81,\n",
       "         81,  34,  48,  62,  47,  54,  55,  59,  71,  98,  41,  44,  38,\n",
       "         61,  83, 106,  79,  61,  52,  57, 103,  63,  47,  99,  39,  46,\n",
       "         47,  54,  47,  86,  48,  96,  75,  98, 104, 112,  80,  69,  39,\n",
       "         55, 108, 107,  87,  92,  52,  39,  54,  58,  47, 102,  62,  76,\n",
       "         44,  59, 118], dtype=int64),\n",
       " array([ 77,  80,  91,  68,  38,  61,  37,  57,  97,  54,  79,  80,  79,\n",
       "         55,  90,  90,  71, 114,  33,  89,  80,  93,  44,  90,  57, 119,\n",
       "         60,  53,  76,  64,  58, 112,  35,  58,  45,  97,  85,  99,  81,\n",
       "        104,  87,  63,  76,  58,  73,  74, 120,  85, 105, 119,  76,  50,\n",
       "         31,  32,  69,  69,  47,  48,  37,  88,  45,  74,  52,  49,  94,\n",
       "         64,  61, 107,  70,  50,  79,  48, 103,  42,  57,  67,  43, 123,\n",
       "         70,  43,  71,  64, 115,  83,  40,  75,  74,  44, 118,  53,  69,\n",
       "         57,  99,  71, 102,  91,  49,  82, 111, 104,  79, 101,  56,  34,\n",
       "         87,  83,  95,  95,  77,  51,  46,  64,  80,  32,  73,  52, 109,\n",
       "         98,  67,  76,  66,  78,  94,  69,  40,  76,  87,  99,  46,  54,\n",
       "        109,  40,  37,  91, 123,  81,  54,  47,  89,  98,  92,  72,  68,\n",
       "        121,  66,  65,  46,  64,  67,  38,  59,  89, 106, 124,  81,  64,\n",
       "        104, 110,  49,  53,  58,  33,  92, 114,  94,  68,  40,  89, 106,\n",
       "         61,  77, 112,  79,  97,  35,  74,  50,  61,  48,  43,  64, 107,\n",
       "         86,  71,  82, 100,  61, 113, 106, 113,  93,  91,  77,  39,  60,\n",
       "         79, 109, 123, 107,  72,  67,  88,  89, 120, 116, 101,  96, 101,\n",
       "         93,  78,  84, 100, 105,  81, 110,  78, 124,  57,  99, 106,  74,\n",
       "        107,  73,  81,  54, 115, 119, 106,  65, 102,  82,  57, 115, 121,\n",
       "         58,  82, 112, 100,  67,  68, 110,  68,  47,  39,  51,  80, 103,\n",
       "         73,  68, 104,  98,  89,  74,  93,  98,  53,  59, 109,  72,  31,\n",
       "        127,  51, 114, 126, 112,  63,  97,  54,  87,  69,  85,  80, 110,\n",
       "        102,  65, 120, 111, 121, 110,  78, 101,  49,  44,  57,  64, 119,\n",
       "        101,  59,  78,  57,  53,  61,  73, 114,  82, 120, 111, 105, 100,\n",
       "         43, 106,  77, 120,  84,  65,  76,  92, 111,  59,  70,  65,  54,\n",
       "        105,  78,  97, 122, 106, 104, 115,  52,  94, 123, 112,  37, 110,\n",
       "         61, 113,  96,  95, 123,  98,  70,  32,  74,  59,  57,  75,  74,\n",
       "         74, 103,  33,  91,  92,  63,  31,  38,  47,  87,  65,  66,  62,\n",
       "         79, 119,  95,  54,  35,  52,  36,  77,  86,  94, 109, 111,  32,\n",
       "        108,  92, 110,  70,  63,  59,  43,  95,  65, 122,  99,  59,  63,\n",
       "         59, 108,  67,  52, 117,  98,  89, 116, 119, 118, 112,  83,  71,\n",
       "         55, 112,  59,  59, 111,  88, 127,  44,  55,  30,  89,  37,  98,\n",
       "         57,  55,  66,  41,  86, 115, 113,  57,  63, 120,  67,  63, 109,\n",
       "         57,  82,  52,  31,  74,  74,  87, 101,  54,  53,  51,  59,  52,\n",
       "         31,  95,  85,  36,  70,  33,  77,  69,  92,  96,  69, 109,  44,\n",
       "        103,  81,  82,  37,  95,  82,  36,  64,  61, 114,  74,  86,  81,\n",
       "        106,  87,  41,  88,  89,  54, 106,  49,  71,  84,  56,  75,  95,\n",
       "        100,  89,  86,  99,  88, 117,  78,  84,  61,  62, 122,  94,  66,\n",
       "         75, 124, 106,  90,  33,  38,  96, 107,  78, 107, 119,  66,  83,\n",
       "        116,  93, 117,  82,  73,  73,  81,  69,  50,  33, 114,  41, 115,\n",
       "         31,  83,  85, 120,  78, 114, 120,  51,  69, 102, 122, 103,  42,\n",
       "         47,  49,  60,  63,  86,  55,  78, 105,  33, 128,  80,  79,  54,\n",
       "         84,  56,  85,  54, 124,  53,  56,  75,  79,  92, 105,  41,  57,\n",
       "        106,  80, 120, 111,  55,  80,  80, 101,  55,  57, 113, 115,  83,\n",
       "         76, 119,  75,  62,  76,  95,  93, 118, 120,  94,  96, 126,  75,\n",
       "        118, 110, 117, 110,  87,  55,  36, 100,  96,  85,  73,  93,  76,\n",
       "         94,  51,  49,  76,  76,  95,  78, 107, 125,  87,  81, 103, 112,\n",
       "         85,  47,  59,  85,  35,  67,  71, 116,  91,  59, 119, 111,  80,\n",
       "         91,  83,  64,  84, 122,  52, 126,  70,  94,  65, 121, 123,  80,\n",
       "        106,  96,  96,  63,  90,  77, 101, 116,  93,  90, 124, 120, 108,\n",
       "         76,  81, 100,  77,  76,  98,  42,  52, 108, 112, 106, 100,  59,\n",
       "         50,  63,  82,  71, 122, 111, 105,  50, 110,  58,  43,  40,  89,\n",
       "        128,  95,  58, 119, 121,  91,  66, 120,  60,  89, 125,  81,  81,\n",
       "         99, 105, 105, 123, 116, 110,  66,  98,  90,  47, 107,  46,  53,\n",
       "        114,  50, 104,  77,  67, 122,  82, 116,  93,  56,  50,  53,  72,\n",
       "        113, 103,  88,  76, 101,  75,  74,  71,  85, 110, 125,  70,  47,\n",
       "         66,  78,  48, 113, 113,  36, 111, 111,  65,  85,  97,  70,  65,\n",
       "         36,  66,  57,  82,  84, 123,  40, 119, 116,  97,  63, 112, 109,\n",
       "         70,  87,  98,  76,  46, 107,  57, 101,  94,  69,  66, 110, 110,\n",
       "        110, 111,  44,  85,  60,  94,  43,  91, 106,  61,  70,  85,  99,\n",
       "         91, 106,  75,  70,  55, 103,  40,  93,  58,  97,  78, 123, 101,\n",
       "         98,  58,  75, 112,  67, 100,  88,  81,  71,  90,  52,  47, 109,\n",
       "        118,  34,  95,  91,  94,  48,  97,  77,  99,  99,  80,  61,  75,\n",
       "        117, 102, 104, 103,  82, 101,  69,  57,  76,  77,  87,  40,  68,\n",
       "        120,  79, 118,  95,  83,  66,  90,  78, 122,  68, 112,  95, 115,\n",
       "         68,  88, 107,  74,  64,  88,  85,  81,  85, 118, 124,  90,  88,\n",
       "         86, 108,  60, 100,  41,  81,  93,  62,  99, 113,  61,  68, 104,\n",
       "         60, 107, 112,  85, 113,  77,  73, 102, 104,  40, 107,  75,  79,\n",
       "         97,  67,  88, 107, 101, 122, 118, 106, 121,  95,  88,  74,  32,\n",
       "         82, 118,  74,  86,  95,  83,  89, 106, 120, 104,  91, 125,  83,\n",
       "         81,  87, 125], dtype=int64))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox_w = bbox_data[:,2]-bbox_data[:,0]\n",
    "bbox_h = bbox_data[:,3]-bbox_data[:,1]\n",
    "bbox_h,bbox_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 70,  77],\n",
       "       [ 66,  80],\n",
       "       [ 86,  91],\n",
       "       ...,\n",
       "       [ 44,  81],\n",
       "       [ 59,  87],\n",
       "       [118, 125]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox_hw = np.stack((bbox_h,bbox_w),axis=1)\n",
    "bbox_hw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.vq import kmeans\n",
    "\n",
    "seed = 729608\n",
    "np.random.seed(seed)\n",
    "iter_time = 100\n",
    "k_list = list(range(1,21,1))\n",
    "distortion_list = []\n",
    "centroid_list = []\n",
    "for k in k_list:\n",
    "    centroid,distortion = kmeans(obs=bbox_hw.astype(np.float32),k_or_guess=k,iter=iter_time)\n",
    "    centroid_list.append(centroid)\n",
    "    distortion_list.append(distortion)\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(np.array(k_list),np.array(distortion_list),marker='*',c=[0.5,0.5,0.])\n",
    "plt.plot(np.array(k_list),np.array(distortion_list),c=[0.5,0.5,0.])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[101.30232 ,  65.9907  ],\n",
       "       [ 84.436264, 106.35694 ],\n",
       "       [ 48.126507,  66.313255]], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_selected = 3\n",
    "centroid = centroid_list[k_selected-1]\n",
    "centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "color_list = ['red','blue','green']\n",
    "marker_list = ['s','o','*']\n",
    "color_list2 = ['pink','purple','gray']\n",
    "\n",
    "\n",
    "distance_0 = np.power(bbox_hw - centroid[0].reshape(1,2),2).sum(axis=1,keepdims=True)\n",
    "distance_1 = np.power(bbox_hw - centroid[1].reshape(1,2),2).sum(axis=1,keepdims=True)\n",
    "distance_2 = np.power(bbox_hw - centroid[2].reshape(1,2),2).sum(axis=1,keepdims=True)\n",
    "\n",
    "distance_matrix = np.concatenate((distance_0,distance_1,distance_2),axis=1)\n",
    "min_indices = distance_matrix.argmin(axis=1)\n",
    "\n",
    "for idx in range(bbox_hw.shape[0]):\n",
    "    plt.scatter(bbox_hw[idx,0],bbox_hw[idx,1],marker=marker_list[min_indices[idx]],color = color_list[min_indices[idx]],linewidth=1)\n",
    "\n",
    "for i in range(3):\n",
    "    plt.scatter(centroid[i,0],centroid[i,1],marker=marker_list[i],linewidth=20,color=color_list2[i])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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